A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines
CEUR Workshop Proceedings
; 3395:354-360, 2022.
Article
in English
| Scopus | ID: covidwho-20240635
ABSTRACT
In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.
BERT classification; COVID-19; Sentiment Analysis; Vaccination; Bayesian networks; Classification (of information); Forestry; Social networking (online); Vaccines; Classification approach; Machine learning models; Multinomial naive bayes; Public sentiments; Random forests; Text classifiers; University of Botswana
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Language:
English
Journal:
CEUR Workshop Proceedings
Year:
2022
Document Type:
Article
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